Buildings with large spaces served by multiple AHUs are common. Conventional control based AHU systems optimize on return air temperature alone and no occupancy information is used. In many buildings, the occupancy is spatially skewed and hence, there is scope to optimize the HVAC energy by operating on the AHU ensemble together. Independent AHU control based on return air temperature is sub-optimal. It is significant to note that heating, ventilation and air conditioning (HVAC) accounts for more than 40% of the total energy consumption in most building types.
Understanding when and where people are present inside a building can help to control the HVAC systems in a better way. There have been works on occupancy based controls that exploit occupancy information for managing IT assets/HVAC energy. These works have typically focused on using occupancy information for purposes such as: on-demand desktop management, lighting control, adjusting fresh air intake, and varying set-point temperatures as per the estimated/observed occupancy.
Occupancy information is obtained through direct sensing, indirect sensing or through using prediction models. Even though, spatial occupancy information sensed at different resolutions are useful in optimizing energy consumption in an area with multi air handling unit setup, conventional PID controllers cannot utilize this information effectively.